---
title: "Predicting In-Hospital MACE After Emergency PCI in STEMI: Machine-Learning Model Using XGBoost"
id: "pubmed-42907224"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42907224"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42907224/"
doi: "10.3760/cma.j.cn112148-20260825-00532"
published_at: "2026-10-24T00:00:00.000Z"
evidence_level: "English Abstract"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Predicting In-Hospital MACE After Emergency PCI in STEMI: Machine-Learning Model Using XGBoost
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42907224
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42907224/)
- **DOI:** [10.3760/cma.j.cn112148-20260825-00532](https://doi.org/10.3760%2Fcma.j.cn112148-20260825-00532)
- **Published At:** 2026-10-24T00:00:00.000Z
- **Evidence Rating:** English Abstract
## Executive GIST (TL;DR)
- Investigators enrolled 5,829 consecutive patients with **STEMI** who underwent emergency PCI from January 1, 2015, to July 1, 2025, at a single tertiary emergency critical care center. - Patients were classified by occurrence of in-hospital major adverse cardiovascular events (MACE): 524 in the MACE group and 5,305 in the non-MACE group. - The cohort was split into a training set (4,080 patients) and a test set (1,749 patients) at a 7:3 ratio for model development and internal validation. - Collected variables included demographics, lifestyle, past medical history, admission vitals, laboratory tests within 24 hours, echocardiographic parameters, Killip class, and procedural data. - Univariate analysis and Lasso regression reduced candidate predictors to 14 features for modeling. - Five machine-learning algorithms were trained: support vector machine (SVM), logistic regression, random forest, naive Bayes, and **XGBoost**. - Model selection used area under the ROC curve (AUC-ROC) and area under the precision-recall curve (AUPRC); calibration and decision curve analysis assessed probability calibration and net clinical benefit. - The **XGBoost** model achieved the highest performance (AUC-ROC 0.992; AUPRC 0.947) and demonstrated good calibration and clinical net benefit on internal testing. - SHAP analysis identified the top five contributors to model predictions: **B-type natriuretic peptide**, creatine kinase-MB, myoglobin, left ventricular ejection fraction, and triglyceride-glucose index. - The authors concluded the model may assist early in-hospital risk stratification for STEMI after emergency PCI; the abstract did not report external validation, hyperparameter settings, or implementation details.
## Clinical Analysis & Structured Key Points
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[ Full text links ](https://pubmed.ncbi.nlm.nih.gov/42907224/) ### Actions Cite Collections Add to Collections * Create a new collection * Add to an existing collection Name your collection: Name must be less than 100 characters Choose a collection: Unable to load your collection due to an error [Please try again](https://pubmed.ncbi.nlm.nih.gov/42907224/) Add Cancel Permalink Permalink Copy Display options Display options Format Abstract PubMed PMID ### Experimental Resource Linked Discoveries ### Page navigation * [ Title & authors ](https://pubmed.ncbi.nlm.nih.gov/42907224/#heading) * [ Abstract ](https://pubmed.ncbi.nlm.nih.gov/42907224/#abstract) * [ Conflict of interest statement ](https://pubmed.ncbi.nlm.nih.gov/42907224/#conflict-of-interest) * [ Publication types ](https://pubmed.ncbi.nlm.nih.gov/42907224/#publication-types) * [ MeSH terms ](https://pubmed.ncbi.nlm.nih.gov/42907224/#mesh-terms) * [ Grants and funding ](https://pubmed.ncbi.nlm.nih.gov/42907224/#grants) * [ LinkOut - more resources ](https://pubmed.ncbi.nlm.nih.gov/42907224/#linkout) Title & authors Abstract Conflict of interest statement Publication types MeSH terms Grants and funding LinkOut - more resources Zhonghua Xin Xue Guan Bing Za Zhi Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Zhonghua+Xin+Xue+Guan+Bing+Za+Zhi%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Zhonghua+Xin+Xue+Guan+Bing+Za+Zhi%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907224/) . 2026 Oct 24;54(10):1190-1197. doi: 10.3760/cma.j.cn112148-20260825-00532. # [Construction of a prediction model for in-hospital major adverse cardiovascular events after emergency percutaneous coronary intervention in patients with ST-elevation myocardial infarction] [Article in Chinese] [Z X Guo](https://pubmed.ncbi.nlm.nih.gov/?term=Guo+ZX&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#full-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China."), [Y H Mi](https://pubmed.ncbi.nlm.nih.gov/?term=Mi+YH&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#full-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China."), [M K Xin](https://pubmed.ncbi.nlm.nih.gov/?term=Xin+MK&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#full-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China."), [X N He](https://pubmed.ncbi.nlm.nih.gov/?term=He+XN&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#full-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.") Affiliations Expand ### Affiliation * 1 Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China. * PMID: **42907224** * DOI: [ 10.3760/cma.j.cn112148-20260825-00532 ](https://doi.org/10.3760/cma.j.cn112148-20260825-00532) Item in Clipboard # [Construction of a prediction model for in-hospital major adverse cardiovascular events after emergency percutaneous coronary intervention in patients with ST-elevation myocardial infarction] [Article in Chinese] Z X Guo et al. Zhonghua Xin Xue Guan Bing Za Zhi. 2026. Show details Display options Display options Format Abstract PubMed PMID Zhonghua Xin Xue Guan Bing Za Zhi Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Zhonghua+Xin+Xue+Guan+Bing+Za+Zhi%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Zhonghua+Xin+Xue+Guan+Bing+Za+Zhi%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907224/) . 2026 Oct 24;54(10):1190-1197. doi: 10.3760/cma.j.cn112148-20260825-00532. ### Authors [Z X Guo](https://pubmed.ncbi.nlm.nih.gov/?term=Guo+ZX&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#short-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China."), [Y H Mi](https://pubmed.ncbi.nlm.nih.gov/?term=Mi+YH&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#short-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China."), [M K Xin](https://pubmed.ncbi.nlm.nih.gov/?term=Xin+MK&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#short-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China."), [X N He](https://pubmed.ncbi.nlm.nih.gov/?term=He+XN&cauthor_id=42907224)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907224/#short-view-affiliation-1 "Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.") ### Affiliation * 1 Emergency Critical Care Centre, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China. * PMID: **42907224** * DOI: [ 10.3760/cma.j.cn112148-20260825-00532 ](https://doi.org/10.3760/cma.j.cn112148-20260825-00532) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID Linked Discoveries ## Abstract in [ English, ](https://pubmed.ncbi.nlm.nih.gov/42907224/#eng-abstract) [ Chinese ](https://pubmed.ncbi.nlm.nih.gov/42907224/#zho-abstract) **Objective:** To construct a machine-learning-based prediction model for in-hospital major adverse cardiovascular events (MACE) among patients with ST-elevation myocardial infarction (STEMI) after emergency percutaneous coronary intervention (PCI). **Methods:** A total of 5829 patients with STEMI who underwent emergency PCI at the Emergency Critical Care Center of Beijing Anzhen Hospital, Capital Medical University, from January 1, 2015, to July 1, 2025, were enrolled. Patients were divided into the MACE group (524 cases) and the non-MACE group (5 305 cases) according to the occurrence of in-hospital MACE and were further divided into the training set (4 080 cases) and test set (1 749 cases) at a ratio of 7∶3. MACE included cardiogenic shock, severe heart failure, severe arrhythmia, acute stent thrombosis or recurrent acute myocardial infarction, non-fatal stroke, and cardiogenic death. Demographic data, lifestyle-related factors, past medical history, admission vital signs, laboratory test results within 24 hours after admission, echocardiographic parameters, Killip classification, and coronary interventional data were collected. Univariate analysis and Lasso regression were performed to select variables for model construction. Five machine-learning _algorithms,_ including support vector machine (SVM), logistic regression, random forest, naive Bayes _,_ and extreme gradient boosting (XGBoost)_,_ were used to construct prediction models for in-hospital MACE. The optimal model was selected based on the area under the receiver-operating characteristic curve (_AUC-ROC_) and the area under the precision-recall curve (_AUPRC_). Calibration curve and decision curve analysis were used to assess the probability calibration and clinical net benefit of the model. SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of each feature. **Results:** The patients were aged (58.1±12.3) years, and 4 875 (83.63%) were male. Fourteen variables were selected for model construction, including B-type natriuretic peptide, myoglobin, left ventricular ejection fraction, high-density lipoprotein cholesterol, high-sensitivity troponin I, triglycerides, triglyceride-glucose index, low-density lipoprotein cholesterol, glycated hemoglobin, creatine kinase-MB, smoking history, heart rate, Killip classification, and age. The XGBoost model yielded the best predictive performance, with an _AUC-ROC_ of 0.992 and an _AUPRC_ of 0.947, and exhibited favorable probability calibration and clinical net benefit. SHAP analysis demonstrated that the top five variables with the greatest contribution to model prediction were B-type natriuretic peptide, creatine kinase-MB, myoglobin, left ventricular ejection fraction, and triglyceride-glucose index. **Conclusions:** Machine-learning prediction models for in-hospital MACE after emergency PCI in patients with STEMI were successfully constructed and internally validated. The XGBoost model demonstrated good discriminative ability, probability calibration, and clinical net benefit and may serve as a reference for early in-hospital risk stratification and clinical decision-making in STEMI patients undergoing emergency PCI. **目的：** 利用机器学习算法构建ST段抬高型心肌梗死（STEMI）患者急诊经皮冠状动脉介入治疗（PCI）术后院内主要不良心血管事件（MACE）预测模型。 **方法：** 入选2015年1月1日至2025年7月1日就诊于首都医科大学附属北京安贞医院急诊危重症中心并接受急诊PCI治疗的STEMI患者5 829例。根据在住院期间是否发生MACE，分为MACE组（524例）和非MACE组（5 305例）。按照7∶3比例将患者分为训练集（4 080例）和测试集（1 749例）。MACE包括心原性休克、严重心力衰竭、严重心律失常、急性支架内血栓形成或再次急性心肌梗死、非致死性卒中及心原性死亡。收集患者人口学、生活行为、既往病史、入院生命体征、入院24 h内实验室检验结果、超声心动图参数、Killip分级等资料。使用单因素分析及Lasso回归分析筛选出用于模型构建的变量。采用支持向量机、逻辑回归、随机森林、朴素贝叶斯、极致梯度提升树（XGBoost）5种机器学习算法构建院内MACE风险预测模型。通过受试者工作特征曲线下面积及精确率-召回率曲线下面积筛选出预测性能最优的模型，通过校准曲线和决策曲线分析评估模型的校准能力及临床净获益。利用SHAP值解释参数贡献大小。 **结果：** 年龄（58.1±12.3）岁，男性4 875例（83.63%）。筛选出B型利钠肽、肌红蛋白、左心室射血分数、高密度脂蛋白胆固醇、高敏肌钙蛋白I、甘油三酯、甘油三酯-葡萄糖指数、低密度脂蛋白胆固醇、糖化血红蛋白、肌酸激酶同工酶、吸烟史、心率、Killip分级和年龄等14个变量。其中基于XGBoost算法构建的模型预测性能最佳（受试者工作特征曲线下面积为0.992，精确率-召回率曲线下面积为0.947），且具有较高的校准度和临床获益。SHAP分析显示，对模型预测结果贡献程度排名前5的变量依次为B型利钠肽、肌酸激酶同工酶、肌红蛋白、左心室射血分数和甘油三酯-葡萄糖指数。 **结论：** 本研究基于机器学习算法成功构建、验证了STEMI患者急诊PCI术后院内MACE的预测模型，其中XGBoost的预测性能最好，具有较好的区分度、校准度和临床净获益，可为STEMI患者急诊PCI术后早期风险分层和临床决策提供参考。. 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